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A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln

Author

Listed:
  • Pin Wu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Lulu Ji

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Wenyan Yuan

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Zhitao Liu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Tiantian Tang

    (Chengdu Xingyun Zhilian Technology Co. Ltd., Chengdu 610000, China)

Abstract

The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln.

Suggested Citation

  • Pin Wu & Lulu Ji & Wenyan Yuan & Zhitao Liu & Tiantian Tang, 2023. "A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:51-:d:1049937
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    References listed on IDEAS

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    1. Kung-Jeng Wang & Ying-Hao Lee & Septianda Angelica, 2021. "Digital twin design for real-time monitoring – a case study of die cutting machine," International Journal of Production Research, Taylor & Francis Journals, vol. 59(21), pages 6471-6485, November.
    2. Michael W. Grieves, 2005. "Product lifecycle management: the new paradigm for enterprises," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 2(1/2), pages 71-84.
    3. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
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